Journal ArticleDOI
Floating search methods in feature selection
TLDR
Sequential search methods characterized by a dynamically changing number of features included or eliminated at each step, henceforth "floating" methods, are presented and are shown to give very good results and to be computationally more effective than the branch and bound method.About:
This article is published in Pattern Recognition Letters.The article was published on 1994-11-01. It has received 3104 citations till now. The article focuses on the topics: Beam search & Jump search.read more
Citations
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Journal ArticleDOI
Semi-supervised SVM for individual tree crown species classification
TL;DR: A novel semi-supervised SVM classifier is presented, specifically developed for tree species classification at individual tree crown (ITC) level, which provides classification accuracies significantly higher than those obtained by the standard supervised SVM and by a state-of-the-art semi- supervisory SVM (S3VM).
Journal ArticleDOI
Texture analysis of carotid artery atherosclerosis from three-dimensional ultrasound images.
TL;DR: Based on DBC, WRS, ACC, and AUC texture features derived from 3D carotid ultrasound were observed to be more sensitive in detecting statin-related changes incarotid atherosclerosis than VWV suggesting that texture classifiers can be used to detect changes inCarotid Atherosclerosis after therapy.
Journal ArticleDOI
A survey of feature selection methods for Gaussian mixture models and hidden Markov models
Stephen Adams,Peter A. Beling +1 more
TL;DR: A review of the literature on feature selection techniques specifically designed for Gaussian mixture models (GMMs) and hidden Markov models (HMMs), two common parametric latent variable models, concludes that further research into unsupervised feature selection methods for HMMs is required and that established methods for GMMs could be adapted to HMMs.
Journal Article
A Comparative Study of Gender and Age Classification in Speech Signals
TL;DR: It is proven that gender classification can be performed with an accuracy of 95\% approximately using speech signal either from both genders or male and female separately, and the accuracy for age classification is about 88%.
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A hybrid model of fuzzy min–max and brain storm optimization for feature selection and data classification
TL;DR: Experimental results indicate that FMM-BSO is able to produce promising results as compared with those from the original FMM network and other state-of-the-art feature selection methods such as particle swarm optimization, genetic algorithm, and ant lion optimization.
References
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Journal ArticleDOI
A Branch and Bound Algorithm for Feature Subset Selection
TL;DR: In this paper, a branch and bound-based feature subset selection algorithm is proposed to select the best subset of m features from an n-feature set without exhaustive search, which is computationally computationally unfeasible.
Journal ArticleDOI
A note on genetic algorithms for large-scale feature selection
W. Siedlecki,Jack Sklansky +1 more
TL;DR: The preliminary results suggest that GA is a powerful means of reducing the time for finding near-optimal subsets of features from large sets.
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A Direct Method of Nonparametric Measurement Selection
TL;DR: A direct method of measurement selection is proposed to determine the best subset of d measurements out of a set of D total measurements, using a nonparametric estimate of the probability of error given a finite design sample set.
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On the effectiveness of receptors in recognition systems
T. Marill,D. Green +1 more
TL;DR: Some of the theoretical problems encountered in trying to determine a more formal measure of the effectiveness of a set of tests are discussed; a measure which might be a practical substitute for the empirical evaluation.
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On automatic feature selection
W. Siedlecki,Jack Sklansky +1 more
TL;DR: In this paper, a review of feature selection for multidimensional pattern classification is presented, and the potential benefits of Monte Carlo approaches such as simulated annealing and genetic algorithms are compared.